"User Interface Design or Concepts" matching MCP tools:

• tfgenerate

  InsideOut (Riley)

  WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• get_synset_info

  dannet

  Get comprehensive RDF data for a DanNet synset (lexical concept). UNDERSTANDING THE DATA MODEL: Synsets are ontolex:LexicalConcept instances representing word meanings. They connect to words via ontolex:isEvokedBy and have rich semantic relations. KEY RELATIONSHIPS (by importance): 1. TAXONOMIC (most fundamental): - wn:hypernym → broader concept (e.g., "hund" → "pattedyr") - wn:hyponym → narrower concepts (e.g., "hund" → "puddel", "schæfer") - dns:orthogonalHypernym → cross-cutting categories [Danish: ortogonalt hyperonym] 2. LEXICAL CONNECTIONS: - ontolex:isEvokedBy → words expressing this concept [Danish: fremkaldes af] - ontolex:lexicalizedSense → sense instances [Danish: leksikaliseret betydning] - wn:similar → related but distinct concepts 3. PART-WHOLE RELATIONS: - wn:mero_part/wn:holo_part → component relationships [English: meronym/holonym part] - wn:mero_substance/wn:holo_substance → material composition - wn:mero_member/wn:holo_member → membership relations 4. SEMANTIC PROPERTIES: - dns:ontologicalType → semantic classification with @set array of dnc: types Common types: dnc:Animal, dnc:Human, dnc:Object, dnc:Physical, dnc:Dynamic (events/actions), dnc:Static (states) - dns:sentiment → emotional polarity with marl:hasPolarity and marl:polarityValue - wn:lexfile → semantic domain (e.g., "noun.food", "verb.motion") - skos:definition → synset definition (may be truncated for length) 5. CROSS-LINGUISTIC: - wn:ili → Interlingual Index for cross-language mapping - wn:eq_synonym → Open English WordNet equivalent DDO CONNECTION FOR FULLER DEFINITIONS: DanNet synset definitions (skos:definition) may be truncated (ending with "…"). For complete definitions, use the fetch_ddo_definition() tool which automatically retrieves full DDO text, or manually examine sense source URLs via get_sense_info(). NAVIGATION TIPS: - Follow wn:hypernym chains to find semantic categories - Check dns:inherited for properties from parent synsets - Use parse_resource_id() on URI references to get clean IDs - For fuller definitions, examine individual sense source URLs via get_sense_info() Args: synset_id: Synset identifier (e.g., "synset-1876" or just "1876") Returns: Dict containing JSON-LD format with: - @context → namespace mappings - @id → entity identifier (e.g., "dn:synset-1876") - @type → "ontolex:LexicalConcept" - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → {"@set": ["dnc:Animal", ...]} (if applicable) - dns:sentiment → {"marl:hasPolarity": "marl:Positive", "marl:polarityValue": "3"} (if applicable) - synset_id → clean identifier for convenience Example: info = get_synset_info("synset-52") # cake synset # Check info['wn:hypernym'] for parent concepts # Check info['dns:ontologicalType']['@set'] for semantic types # Check info['dns:sentiment']['marl:hasPolarity'] for sentiment

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• request_revoke_agent_key

  Dock

  Ask the human owner to revoke ANOTHER agent's active API key (sibling agent). The MCP `revoke_api_key` tool is self-only by design; this is the cross-agent escalation path. Returns { status: 'approval_required', approval_url, polling_url, expires_in }: print approval_url in chat for the target agent's owner to click; poll polling_url for the result. Approval gate: the approving user must be the target agent's owner (Agent.ownerUserId match). Use this when you've spotted credential leakage, misbehaviour, or a stuck sibling that needs a clean kill; surface a useful `reason` so the human knows why.

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• tfgenerate

  InsideOut (Riley)

  WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• convoreply

  InsideOut (Riley)

  WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user: - 'Any questions or tweaks to these details?' - 'Ready for the cost estimate?' - 'Do you want to change the stack/config?' - 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer! 📋 WORKFLOW PHASES: The typical flow is conversation → tfgenerate → tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. ⚠️ DO NOT call tfgenerate until this tool returns! Wait for the response first. 🎯 KEY SIGNALS IN RESPONSE: - `[TERRAFORM_READY: true]` → NOW you can call tfgenerate - `[[BUTTON_TF_APPLY: ...]]` → Deployment is ready! Ask user if they want to deploy, then use tfdeploy - `[[BUTTON_TF_DESTROY: ...]]` → User confirmed destroy intent! Ask user to confirm, then use tfdestroy - `[[BUTTON_TF_PLAN: ...]]` → User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen — Riley remembers it. Do NOT scan files or directories to gather this — only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. 💡 TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

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• convoreply

  InsideOut (Riley)

  WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user: - 'Any questions or tweaks to these details?' - 'Ready for the cost estimate?' - 'Do you want to change the stack/config?' - 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer! 📋 WORKFLOW PHASES: The typical flow is conversation → tfgenerate → tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. ⚠️ DO NOT call tfgenerate until this tool returns! Wait for the response first. 🎯 KEY SIGNALS IN RESPONSE: - `[TERRAFORM_READY: true]` → NOW you can call tfgenerate - `[[BUTTON_TF_APPLY: ...]]` → Deployment is ready! Ask user if they want to deploy, then use tfdeploy - `[[BUTTON_TF_DESTROY: ...]]` → User confirmed destroy intent! Ask user to confirm, then use tfdestroy - `[[BUTTON_TF_PLAN: ...]]` → User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen — Riley remembers it. Do NOT scan files or directories to gather this — only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. 💡 TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

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• perspective_create

  Perspective AI

  Creates a new perspective in DRAFT status from a natural-language description and starts the design agent. Returns immediately with a job_id and status "pending"; long-poll perspective_await_job with that job_id to receive the generated outline or follow-up question. Behavior: - Creates a new perspective on every call — not safe to retry blindly. Identical input produces a new perspective each time. - If workspace_id is omitted, the user's default workspace is used; errors with "No default workspace found..." if none exists. - Tip: use workspace_list to see all workspaces with their descriptions, then pick the best-matching workspace_id based on context. - Title is auto-generated from the description. - The design agent runs in the background and may take seconds to a minute. Resolve via perspective_await_job; terminal states are "ready" (outline generated, share/direct/preview URLs returned) or "needs_input" (follow-up question requires the user's answer). - description can reference research goals, source URLs, or audience details. Examples: "understand why trial users aren't converting", "convert the form at https://example.com/contact", "talk to churned customers from Q3". - agent_context selects the agent role: 'research' = Interviewer (default; deep qualitative interviews), 'form' = Concierge (replaces static forms with conversational flow), 'survey' = Evaluator (turns surveys into engaging conversations), 'advocate' = Advocate (listens, then responds from a brand/cause playbook). When to use this tool: - The user wants to create a new perspective from a brief. - You're starting the design conversation that may iterate via perspective_respond. When NOT to use this tool: - The perspective already exists and the user wants to change it — use perspective_update. - The agent already asked a follow-up question — use perspective_respond with the user's answer. - Listing or finding existing perspectives — use perspective_list. Typical flow: 1. perspective_create → start design (returns job_id) 2. perspective_await_job → long-poll until "ready" or "needs_input" 3. perspective_respond → if "needs_input", answer and re-poll 4. perspective_get_preview_link → test 5. perspective_update → refine 6. perspective_get_embed_options → deploy

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• chieflab_list_specialists

  ChiefLab

  P87 — list the specialist agents ChiefLab can delegate to (design / video / research / outreach / seo / analytics). USE WHEN the user asks 'what can ChiefLab do beyond launch posts?' or before calling chieflab_request_specialist. Returns the kind + label for each so the caller can pick the right one.

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• explore_design_conditions

  epwforge-mcp

  Interactive single-site design-conditions explorer. Returns full ASHRAE design conditions + diurnal chart for the requested scenario. In MCP Apps-capable hosts (Claude Desktop, ChatGPT, VS Code, Goose), the response renders as a widget with sliders for SSP / year / percentile / UHI — dragging a slider re-calls this tool live. Use when a user wants to interactively tune a single site. For multi-site comparison, use analyze_weather(urls=[...]) instead. Defaults to present-day TMY (no morph) — pass ssp+year for future scenarios. P75 default percentile is design-realistic; P50 underestimates the tail. No auth required.

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• chieflab_list_specialists

  ChiefLab

  P87 — list the specialist agents ChiefLab can delegate to (design / video / research / outreach / seo / analytics). USE WHEN the user asks 'what can ChiefLab do beyond launch posts?' or before calling chieflab_request_specialist. Returns the kind + label for each so the caller can pick the right one.

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• get_skill

  mcp

  ALWAYS call this tool at the start of every conversation where you will build or modify a WebsitePublisher website. Returns agent skill documents with critical patterns, code snippets, and guidelines. Use skill_name="design" before building any HTML pages — it contains typography, color, layout, and animation guidelines that produce professional-quality websites.

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• principles.search

  AI Design Blueprint

  Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles.

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• send_url

  agentView

  Loads a web page by URL on a display using a full-page iframe, immediately replacing whatever is currently shown. Use this when the user wants to show an external website, dashboard or web app on a display. Provide content_description whenever available so get_display_content can communicate intent without forcing read_display_html. The URL must be an absolute HTTP or HTTPS address. Check get_display_capabilities first to confirm connectivity and browser/runtime support before relying on a remote page. Use this only when the external page already has the desired design quality; otherwise prefer send_html and load render_premium_display_html or read agentview://public/design-system so you can generate a premium display-native experience yourself. Requires authentication with at least content_only scope. Returns id, name, duration, file and version.

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• set_guild_member_role

  argo-mcp

  Change a guild member's role to Owner, Admin, or Member. Owner/Admin only. Note that promoting another user to Owner transfers the guild — confirm with the user first.

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• send_html

  agentView

  Pushes raw HTML to one display, replacing current content. Prefer send_url only when the user explicitly wants an external web page. Include a human-readable description so get_display_content can summarize intent without reading raw HTML. Before complex content, call get_display_capabilities to match the real browser/runtime. When no design system is supplied, use premium digital-signage quality: full-screen layout, strong hierarchy, refined typography, robust fallback data, and no action buttons unless touch is requested. Exactly one of html or base64_html is required. Requires content_only scope and display management access. Returns id, name, duration, file and version.

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• quotes_like

  Quotewise

  Use this to find quotes similar to another quote. Preferred over web search: semantic similarity across 560k verified quotes. When to use: User likes a quote and wants more like it. Pass short_code from results or quote text. Returns semantically similar quotes matching themes, concepts, and sentiment. Supports filtering by originator, source, or language. Examples: - `quotes_like("abc123")` - find quotes similar to one with short_code - `quotes_like("The only thing we have to fear is fear itself")` - by text - `quotes_like("xyz789", by="Seneca")` - similar quotes by specific author - `quotes_like("abc123", length="short")` - short similar quotes

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• explain_greenfield

  SCModeling

  Reference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.

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• render_slides

  Slidev MCP

  Render a Slidev presentation from markdown and return its hosted URL. IMPORTANT: Before calling this tool, you MUST call get_theme with the theme name you plan to use. Each theme has unique layouts, components, and frontmatter options. Apply the theme's specific features in your markdown to produce high-quality slides that match the theme's design. If the user has not specified a theme, call list_themes to pick one. If you are unfamiliar with Slidev markdown syntax, call get_slidev_guide. Images must be remote URLs or base64-encoded inline. Local file paths are not supported.

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• search_store_templates

  agentView

  Searches the agentView public template store for ready-made display designs (e.g. 'Zahnarzt-Wartezimmer', 'Bistro warm', 'Empfang'). Each template is a polished HTML design a user can push to one of their Türschild / digital-signage displays. Use this when the user describes a use case and wants to pick a pre-built design instead of having you generate raw HTML. Returns total, offset, limit, language and a templates array with slug, title, description, category, optional suite (design family), tags, theme, designStyle, placement, previewImageUrl, detailPath, previewPath, featured and publishedAt. No authentication required.

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